6 papers
Does PCA Work for Rough Functional Data?
Tim Kutta, Nina Dörnemann, Piotr Kokoszka
Functional data analysis is concerned with the analysis of infinite-dimensional data functions. Functional principal component analysis (FPCA) is a key method to obtain finite-dime…
Monitoring for a Phase Transition in a Time Series of Wigner Matrices
Nina Dörnemann, Piotr Kokoszka, Tim Kutta +1
We develop methodology and theory for the detection of a phase transition in a time-series of high-dimensional random matrices. In the model we study, at each time point \( t = 1,2…
Two-Sample Covariance Inference in High-Dimensional Elliptical Models
Nina Dörnemann
We propose a two-sample test for large-dimensional covariance matrices in generalized elliptical models. The test statistic is based on a U-statistic estimator of the squared Frobe…
A New Two-Sample Test for Covariance Matrices in High Dimensions: U-Statistics Meet Leading Eigenvalues
Thomas Lam, Nina Dörnemann, Holger Dette
We propose a two-sample test for covariance matrices in the high-dimensional regime, where the dimension diverges proportionally to the sample size. Our hybrid test combines a Frob…
Tracy-Widom, Gaussian, and Bootstrap: Approximations for Leading Eigenvalues in High-Dimensional PCA
Nina Dörnemann, Miles E. Lopes
Under certain conditions, the largest eigenvalue of a sample covariance matrix undergoes a well-known phase transition when the sample size and data dimension diverge propo…
Linear spectral statistics of sequential sample covariance matrices
Nina Dörnemann, Holger Dette
Independent -dimensional vectors with independent complex or real valued entries such that , ,…